The integration of data‑centric artificial intelligence (AI) into governance mechanisms signifies a transformative epoch in how welfare systems are designed, implemented, and regulated. This paper develops a comprehensive framework that explicates the theoretical underpinnings, practical mechanisms, ethical implications, and governance models that promote transparency, equity, bias mitigation, and policy compliance within welfare management. Against the backdrop of a growing scholarly consensus on the imperative of data quality, integrity, and governance as central to AI effectiveness (Priyadarshi et al., 2026), this research synthesizes cross‑disciplinary perspectives from data science, public policy, AI ethics, and systems engineering. We interrogate the technological, organizational, and socio‑political dimensions of data‑centric AI, situating welfare governance in an ecosystem where data governance functions as both a technical and normative anchor for accountability. Through analytical integration of scholarly debates, this study constructs an interpretive model that aligns data‑centric paradigm principles with the unique demands of public welfare systems. Findings reveal critical intersections between data governance and AI trustworthiness, emphasizing the necessity of robust data curation, participatory policy frameworks, continual evaluation mechanisms, and stakeholder alignment. The discussion foregrounds complex debates around fairness, bias control, regulatory compliance, and transparency, offering a distilled yet expansive discourse that bridges theory and practice. The paper concludes with an actionable research agenda that underscores emergent questions for future inquiry.

data‑centric artificial intelligence, governance, transparency, bias mitigation

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